Most of the above methods rely on internal activations acquired by utilizing activation maps at different layers of the networks. Given their intrinsic properties (e.g., local receptive field), CNNs decompose object into local semantic elements [1]}, [2]}. These intrinsic properties prevent the CNN from forming global relationships between different receptive fields, limiting their ability to localize whole object, and producing coarse blobby maps focused on discriminant regions. One solution to form long-range dependencies is to find global cues by calculating pixel-similarities [3]}, [4]}, [5]}, [6]}. More recently, [7]} propose a method based on non-local attention blocks to build long-range relationships and efficiently localize objects. It enhances attention maps by incorporating spatial similarity.
